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Support vector machine classification using semi-parametric model

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Abstract

Pattern recognition and data mining using support vector machine (SVM) have been the focus of widespread researches in recent decades. In SVM, a hyper-plane is designed to classify the training data. A challenge in SVM is that the parameters of hyper-planes are constants. As a result, there may be some critical points that will be classified into a wrong set. It should be mentioned that finding this hyper-plane is very similar to solving a regression problem using parametric or semi-parametric models in statistics. This is the main motivation of this paper. The contribution of this paper is combining SVM classifier and semi-parametric models (SP-SVM) to solve the aforementioned challenge. In fact, using semi-parametric linear model results in some serial linear decision boundaries with several slopes and intercepts. In other words, there are two types of kernels in the proposed SP-SVM: the kernels that perform nonlinear transformation of the input features and the kernels needed in the semi-parametric model. The validations have been done on Iris data set and also some other linearly non-separable classification problems. The accuracy of the proposed SP-SVM outperforms some related algorithms such as K-nearest neighbor (KNN)-based weighted multi-class twin support vector machines (KWMTSVM), support vector classification–regression machine for K-class classification (K-SVCR), twin multi-class classification support vector machines (twin-KSVC), intelligent particle swarm classifier (IPS-classifier) and random forest. The accuracy of SP-SVM is 97.33%. Thus, SP-SVM can play an important role in increasing the accuracy of industrial machines that perform classifications, for example, agricultural products.

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Acknowledgements

Authors declare that the manuscript has not been submitted to more than one journal for simultaneous consideration and it has not been published previously (partly or in full). In addition, this study has not been split up into several parts to increase the quantity of submissions. No data have been fabricated or manipulated to support the conclusions. Moreover, no data, text or theories by others are presented as if they were our own. Proper acknowledgements to other works have been given.

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Authors and Affiliations

  1. Department of Statistics, University of Birjand, 97175/615, Birjand, Iran

    Mohammad Ghassem Akbari

  2. Faculty of Electrical and Computer Engineering, University of Birjand, 97175/615, Birjand, Iran

    Saeed Khorashadizadeh & Mohammad-Hassan Majidi

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  1. Mohammad Ghassem Akbari
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  2. Saeed Khorashadizadeh
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  3. Mohammad-Hassan Majidi
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Contributions

MGA developed the main ideas, generated the formulation of the algorithm and performed the experiments. SK provided the English text of the paper, edited the formulations and simulations, provided the reply letter and revised the manuscript. MM provided the references for literature review and proposed some experiments.

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Correspondence to Saeed Khorashadizadeh.

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Akbari, M., Khorashadizadeh, S. & Majidi, MH. Support vector machine classification using semi-parametric model. Soft Comput 26, 10049–10062 (2022). https://doi.org/10.1007/s00500-022-07376-2

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